Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	35,724
2	Florida	32,617
3	Mississippi	32,545
4	Alabama	31,065
5	Arizona	29,845
6	South Carolina	28,334
7	Georgia	28,097
8	Tennessee	27,827
9	Iowa	27,455
10	North Dakota	27,198
11	Arkansas	26,920
12	Texas	26,574
13	Nevada	25,589
14	South Dakota	24,349
15	New York	23,663
16	New Jersey	23,118
17	Illinois	22,985
18	Rhode Island	22,826
19	Nebraska	22,778
20	Idaho	22,663
21	Utah	22,026
22	District of Columbia	21,608
23	Oklahoma	21,312
24	Wisconsin	21,038
25	Delaware	20,805
26	California	20,542
27	Missouri	20,501
28	Maryland	20,493
29	Kansas	19,891
30	North Carolina	19,775
31	Massachusetts	18,954
32	Indiana	17,783
33	Minnesota	17,161
34	Virginia	17,121
35	Connecticut	15,871
36	Kentucky	15,754
37	Puerto Rico	14,498
38	New Mexico	13,756
39	Michigan	13,458
40	Ohio	12,901
41	Pennsylvania	12,558
42	Colorado	12,063
43	Washington	11,795
44	Alaska	11,426
45	Montana	11,345
46	Wyoming	9,732
47	Hawaii	8,661
48	West Virginia	8,564
49	Oregon	7,781
50	New Hampshire	6,010
51	Maine	3,933
52	Vermont	2,791

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	556
2	South Dakota	544
3	Wisconsin	443
4	Utah	358
5	Alabama	279
6	Iowa	277
7	Oklahoma	260
8	North Carolina	259
9	Puerto Rico	256
10	Missouri	254
11	Arkansas	242
12	Tennessee	239
13	Montana	232
14	Nebraska	229
15	Minnesota	219
16	South Carolina	205
17	Wyoming	188
18	Idaho	177
19	Illinois	174
20	Kansas	173
21	Mississippi	173
22	Indiana	160
23	Kentucky	160
24	Nevada	160
25	Alaska	156
26	Texas	139
27	Georgia	125
28	West Virginia	119
29	Florida	116
30	Louisiana	116
31	Delaware	107
32	Virginia	103
33	Colorado	99
34	New Mexico	98
35	Maryland	88
36	Ohio	87
37	California	84
38	Hawaii	83
39	Massachusetts	79
40	Oregon	75
41	New Jersey	74
42	District of Columbia	68
43	Michigan	67
44	Pennsylvania	66
45	Arizona	63
46	Washington	53
47	New York	47
48	New Hampshire	31
49	Maine	18
50	Connecticut	10
51	Vermont	9
52	Rhode Island	-40

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,813
2	New York	1,682
3	Massachusetts	1,364
4	Connecticut	1,262
5	Louisiana	1,175
6	Rhode Island	1,044
7	Mississippi	980
8	District of Columbia	884
9	Arizona	772
10	Michigan	705
11	Illinois	699
12	Florida	653
13	Delaware	650
14	Maryland	650
15	South Carolina	645
16	Georgia	639
17	Pennsylvania	638
18	Texas	545
19	Indiana	531
20	Nevada	514
21	Alabama	510
22	Arkansas	433
23	Iowa	417
24	New Mexico	414
25	Ohio	405
26	California	395
27	Virginia	370
28	Minnesota	365
29	Colorado	355
30	Missouri	344
31	Tennessee	344
32	North Carolina	329
33	New Hampshire	322
34	North Dakota	307
35	Washington	288
36	Kentucky	266
37	Idaho	257
38	Oklahoma	254
39	Nebraska	250
40	South Dakota	246
41	Wisconsin	221
42	Kansas	218
43	Puerto Rico	201
44	West Virginia	186
45	Montana	161
46	Utah	141
47	Oregon	130
48	Maine	104
49	Hawaii	92
50	Vermont	92
51	Wyoming	86
52	Alaska	71

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	North Dakota	8
2	Arkansas	6
3	Mississippi	5
4	Alaska	4
5	Florida	3
6	Georgia	3
7	Louisiana	3
8	Missouri	3
9	South Carolina	3
10	South Dakota	3
11	Tennessee	3
12	Arizona	2
13	Massachusetts	2
14	Montana	2
15	Nevada	2
16	North Carolina	2
17	Oklahoma	2
18	Texas	2
19	California	1
20	Delaware	1
21	District of Columbia	1
22	Hawaii	1
23	Illinois	1
24	Indiana	1
25	Iowa	1
26	Kentucky	1
27	Maryland	1
28	Minnesota	1
29	Nebraska	1
30	New Mexico	1
31	Puerto Rico	1
32	Virginia	1
33	West Virginia	1
34	Alabama	0
35	Colorado	0
36	Connecticut	0
37	Idaho	0
38	Kansas	0
39	Maine	0
40	Michigan	0
41	New Hampshire	0
42	New Jersey	0
43	New York	0
44	Ohio	0
45	Oregon	0
46	Pennsylvania	0
47	Rhode Island	0
48	Utah	0
49	Vermont	0
50	Washington	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Lincoln	Arkansas	154,561	1	99
Chattahoochee	Georgia	149,720	2	99
Trousdale	Tennessee	147,643	3	99
Lafayette	Florida	146,402	4	99
Lake	Tennessee	131,842	5	99
Davidson	Tennessee	41,278	195	93
Richland	South Carolina	36,680	270	91
York	South Carolina	19,115	1163	62
Orange	California	17,249	1336	57
Pierce	Washington	9,731	2207	29

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	4,966	1	99
Kenedy	Texas	4,950	2	99
Emporia city	Virginia	4,863	3	99
Galax city	Virginia	4,727	4	99
Randolph	Georgia	4,279	5	99
Richland	South Carolina	577	694	77
Davidson	Tennessee	441	969	69
Orange	California	383	1108	64
York	South Carolina	267	1444	54
Pierce	Washington	246	1505	52

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.